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        <span>分层抽样和欠拟合或过拟合</span>
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        <h1 id="分层抽样和欠拟合或过拟合"><a href="#分层抽样和欠拟合或过拟合" class="headerlink" title="分层抽样和欠拟合或过拟合"></a>分层抽样和欠拟合或过拟合</h1><h2 id="抽样方式"><a href="#抽样方式" class="headerlink" title="抽样方式"></a>抽样方式</h2><h3 id="随机抽样"><a href="#随机抽样" class="headerlink" title="随机抽样"></a>随机抽样</h3><p>总的数据比例</p>
<table>
<thead>
<tr>
<th>slp03</th>
<th>78</th>
<th>307</th>
<th>105</th>
<th>74</th>
<th>114</th>
</tr>
</thead>
<tbody><tr>
<td>总比例</td>
<td>0.11504</td>
<td>0.45280</td>
<td>0.154867</td>
<td>0.109144</td>
<td>0.168141</td>
</tr>
<tr>
<td>测试集随机抽样的比例</td>
<td>0.132352</td>
<td>0.431372</td>
<td>0.127450</td>
<td>0.093137</td>
<td>0.215686</td>
</tr>
<tr>
<td>测试集分层抽样的比例</td>
<td>0.117647</td>
<td>0.450980</td>
<td>0.156862</td>
<td>0.107843</td>
<td>0.166666</td>
</tr>
</tbody></table>
<p>因此采用分层抽样去进行比例的划分</p>
<p>X_train, X_test, y_train, y_test = train_test_split(df, label, <strong>stratify=label,</strong> test_size=0.3)</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/8/27</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">from</span> data_preprocessing <span class="keyword">import</span> *</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestClassifier</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> accuracy_score</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">run_rf</span>(<span class="params">xtrain, xtest, ytrain, ytest</span>):</span></span><br><span class="line">    clf = RandomForestClassifier()</span><br><span class="line">    clf.fit(xtrain, ytrain)</span><br><span class="line">    ypred = clf.predict(xtest)</span><br><span class="line">    sad = accuracy_score(ytest, ypred)</span><br><span class="line">    <span class="keyword">return</span> sad</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取数据名字</span></span><br><span class="line">text_name = pd.read_excel(<span class="string">&#x27;F:/slpdb_data/mitdata/slpdb_name.xlsx&#x27;</span>)</span><br><span class="line">slpdb_name = np.array(text_name[<span class="string">&#x27;name&#x27;</span>]).tolist()</span><br><span class="line"></span><br><span class="line">se = []</span><br><span class="line"><span class="keyword">for</span> text <span class="keyword">in</span> slpdb_name[<span class="number">4</span>:<span class="number">5</span>]:</span><br><span class="line">    <span class="comment"># 打开对应的特征和对应的集合</span></span><br><span class="line">    feature = pd.read_excel(<span class="string">&#x27;F:/py/py_sleep stage and apnea/data/slpdb_feature/features_&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % text + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">    data = pd.get_dummies(feature.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature), <span class="number">1</span>:])</span><br><span class="line">    labels = pd.read_excel(<span class="string">&#x27;F:/py/py_sleep stage and apnea/data/slpdb_note/note_&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % text + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">    tag = pd.get_dummies(labels.iloc[<span class="number">0</span>:<span class="built_in">len</span>(data), <span class="number">1</span>:])</span><br><span class="line">    <span class="comment"># 均值插补和zscore标准化</span></span><br><span class="line">    df = data_pre(data)</span><br><span class="line">    <span class="comment"># 读取想要分期的标签0-1:5期，1-2：4期，2-3:3期</span></span><br><span class="line">    label = pd.get_dummies(tag.iloc[<span class="number">0</span>:<span class="built_in">len</span>(data), <span class="number">0</span>:<span class="number">1</span>])</span><br><span class="line">    X_train, X_test, y_train, y_test = train_test_split(df, label, stratify=label, test_size=<span class="number">0.3</span>)</span><br><span class="line">    <span class="comment"># 查看y的分类比例和总体比例</span></span><br><span class="line">    <span class="comment"># 总体比例</span></span><br><span class="line">    list_y_test = np.array(y_test)</span><br><span class="line">    num_3 = <span class="number">0</span></span><br><span class="line">    num_2 = <span class="number">0</span></span><br><span class="line">    num_1 = <span class="number">0</span></span><br><span class="line">    num_r = <span class="number">0</span></span><br><span class="line">    num_w = <span class="number">0</span></span><br><span class="line"></span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(list_y_test)):</span><br><span class="line">        <span class="keyword">if</span> list_y_test[i][<span class="number">0</span>] == <span class="number">1</span>:</span><br><span class="line">            num_3 += <span class="number">1</span></span><br><span class="line">        <span class="keyword">elif</span> list_y_test[i][<span class="number">0</span>] == <span class="number">2</span>:</span><br><span class="line">            num_2 += <span class="number">1</span></span><br><span class="line">        <span class="keyword">elif</span> list_y_test[i][<span class="number">0</span>] == <span class="number">3</span>:</span><br><span class="line">            num_1 += <span class="number">1</span></span><br><span class="line">        <span class="keyword">elif</span> list_y_test[i][<span class="number">0</span>] == <span class="number">4</span>:</span><br><span class="line">            num_r += <span class="number">1</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            num_w += <span class="number">1</span></span><br><span class="line">    print(num_3/<span class="built_in">len</span>(list_y_test))</span><br><span class="line">    print(num_2/<span class="built_in">len</span>(list_y_test))</span><br><span class="line">    print(num_1/<span class="built_in">len</span>(list_y_test))</span><br><span class="line">    print(num_r/<span class="built_in">len</span>(list_y_test))</span><br><span class="line">    print(num_w/<span class="built_in">len</span>(list_y_test))</span><br></pre></td></tr></table></figure>



<h2 id="过拟合和欠拟合问题"><a href="#过拟合和欠拟合问题" class="headerlink" title="过拟合和欠拟合问题"></a>过拟合和欠拟合问题</h2><p>随机森林一般用更高的偏差去替换低的方差。因此只需要多加点决策树，去满足特征集合就可以，不需要再去考虑这个拟合的问题。</p>
<h2 id="特征的确定"><a href="#特征的确定" class="headerlink" title="特征的确定"></a>特征的确定</h2><ol>
<li>特征集合预处理：均值插补缺省值和zscore 标准化</li>
<li>利用随机森林获取特征重要度</li>
<li>然后筛选出重要度高于平均值的46个特征</li>
<li>再利用46个特征去获取平均重要度和得到这46个特征下特征递归算法，去获取特征的准确率</li>
<li>所以现在就是平均特征重要度确定后，用了一下PCA，重要的就17个啥的就可以了</li>
<li>先看图嘛，然后再去看下结果</li>
</ol>
<h1 id="特征为17个特征"><a href="#特征为17个特征" class="headerlink" title="特征为17个特征"></a>特征为17个特征</h1><p>经过了slpdb和ucddb效果都很好</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">[<span class="string">&#x27;HR_mean&#x27;</span>, <span class="string">&#x27;R_mean&#x27;</span>, <span class="string">&#x27;HR_max&#x27;</span>, <span class="string">&#x27;5HR_mean&#x27;</span>, <span class="string">&#x27;5R_mean&#x27;</span>, <span class="string">&#x27;R_median&#x27;</span>,</span><br><span class="line">       <span class="string">&#x27;5p_Impulse_factor&#x27;</span>, <span class="string">&#x27;5p_Margin_factor&#x27;</span>, <span class="string">&#x27;5p_max&#x27;</span>,</span><br><span class="line">       <span class="string">&#x27;5p_peak_factor&#x27;</span>, <span class="string">&#x27;5p_Peaks&#x27;</span>, <span class="string">&#x27;5p_median&#x27;</span>, <span class="string">&#x27;5csi10&#x27;</span>, <span class="string">&#x27;5p_mean&#x27;</span>,</span><br><span class="line">       <span class="string">&#x27;5p_skew&#x27;</span>, <span class="string">&#x27;5apen&#x27;</span>, <span class="string">&#x27;5p_RMS&#x27;</span>], dtype=<span class="built_in">object</span>)</span><br></pre></td></tr></table></figure>

<p>下一步看分类器效果并将分类器写成函数，然后确定一个进行评估，评估完后才是分成两类，然后再去分别优化</p>
<p><img src="https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1598861004182&di=be372ad9e9bd00bf49d749c80fea3a1b&imgtype=0&src=http://zymsc.bs2cdn.yy.com/d08c16b9-a99c-4f55-b4e8-e4b6ff8f79ea.png"></p>
<p><img src="https://ss1.bdstatic.com/70cFuXSh_Q1YnxGkpoWK1HF6hhy/it/u=2384619687,1414757876&fm=26&gp=0.jpg"></p>

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